Semi-Empirical Prediction of Pesticide Loading in the Sacramento and San Joaquin Rivers during Winter Storm Seasons

Transport of pesticides by surface runoff during rainfall events is a major process contributing to pesticide contamination in rivers. This study presents an empirical regression model that describes pesticide loading over time in the Sacramento and San Joaquin Rivers. The empirical model is physically based, but uses highly aggregated parameters, allowing the prediction of pesticide loading with the knowledge of precipitation and pesticide use only. The model was applied to analyze pesticide monitoring data obtained from the two California rivers during various 1991-2000 winter storm seasons, and closely simulated loading dynamics of the pesticides for six out of seven cases studied, which involved four pesticides identified based on historical sampling results with a detection frequency of ≥10%. The coefficients of determination for regression ranged from 0.167 to 0.907, all were significant at <0.001. The unresolved discrepancy between the model and data may be attributable to a number of sources including limitations related to sampling, laboratory analysis, pesticide use reporting as well as model formulation etc. The accuracy of the model predictions, however, are well within the limits of the expected model performance, given the time and spatial scales of the data analyzed. The results of this study provide strong evidence that precipitation and pesticide use are the two major environmental variables dictating the dynamics of pesticide transport into surface water in these watersheds. The capability of the statistical model to provide time-series estimates on pesticide loading in rivers is unique and may be useful for Total Maximum Daily Load (TMDL) assessments.